ImportError: cannot import name 'evaluate' ( from surprise import evaluate )

Question

from surprise import Reader, Dataset, SVD
from surprise import evaluate
---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
<ipython-input-12-6d771df269b8> in <module>()
----> 1 from surprise import evaluate

ImportError: cannot import name 'evaluate'

第一行，from surprise import Reader, Dataset, SVD 工作正常。就是这样，它无法从 surprise 包中导入评估。

我已经使用 conda 安装了 scikit-surprise。我认为它已安装成功。

Answer 1

根据文档，evaluate() 方法是 deprecated in version 1.0.5 (functionally replaced by model_selection.cross_validate()) and was removed in version 1.1.0，这很可能是您安装的方法。

Answer 2

自 2020 年 1 月起，改为执行以下操作...

from surprise import SVD
from surprise import Dataset
from surprise.model_selection import cross_validate

# Load the dataset (download it if needed)
data = Dataset.load_builtin('ml-100k')

# Use the famous SVD algorithm
algo = SVD()

# Run 5-fold cross-validation and then print results
cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)

Answer 3

如@merv 所述，evaluate() 方法在 1.0.5 版中已弃用。这是一个使用 scikit-surprise==1.1.1:

测试的工作示例

import pandas as pd
from surprise import SVD, Reader
from surprise import Dataset
from surprise.model_selection import cross_validate

reader = Reader()
csv = pd.read_csv('yourdata.csv')

# Loading local dataset
data = Dataset.load_from_df(csv, reader)

# Use SVD algorithm or other models
model = SVD()

# cross-validation with no. of kfold=5 (can be changed per your need)
cross_validate(model, data, measures=['rmse', 'mae'], cv=5)

训练模型

data_train = data.build_full_trainset()
model.fit(data_train)

预测

model.predict(uid=<e.g. 1>, iid=<e.g. 2>)

您可以阅读 documentation 了解更多详情。

ImportError: cannot import name 'evaluate' ( from surprise import evaluate )

ImportError: cannot import name 'evaluate' ( from surprise import evaluate )

python-3.x

scikit-learn

anaconda

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